Beyond simulation and algorithm development,
many developers increasingly use MATLAB even for product deployment in
computationally heavy fields. This often demands that MATLAB codes run
faster by leveraging the distributed parallelism of Graphics Processing
Units (GPUs). While MATLAB successfully provides high-level functions as
a simulation tool for rapid prototyping, the underlying details and
knowledge needed for utilizing GPUs make MATLAB users hesitate to step
into it. Accelerating MATLAB with GPUs offers a primer on bridging this gap. Starting
with the basics, setting up MATLAB for CUDA (in Windows, Linux and Mac
OS X) and profiling, it then guides users through advanced topics such
as CUDA libraries. The authors share their experience developing
algorithms using MATLAB, C++ and GPUs for huge datasets, modifying
MATLAB codes to better utilize the computational power of GPUs, and
integrating them into commercial software products. Throughout the
book, they demonstrate many example codes that can be used as templates
of C-MEX and CUDA codes for readers’ projects. Download example codes
from the publisher's website:
http://booksite.elsevier.com/9780124080805/. It includes: Shows how to accelerate MATLAB codes through the GPU for parallel processing, with minimal hardware knowledge + Explains the related background on hardware, architecture and programming for ease of use + Provides simple worked examples of MATLAB and CUDA C codes as well as templates that can be reused in real-world projects.